We compare our LBF with several representative point cloud denoising methods, including BF [1]}, WLOP [2]}, EC-Net [3]}, PF [4]} and RePCD [5]}.
To quantitatively compare these methods, we calculate the Chamfer distance (CD) and Mean Square Error (MSE) for the synthetic noisy models in the test dataset, as reported in Table REF . It can be observed that the proposed LBF achieves the best results among all the competitors.
Besides, we also present the visual comparisons on both synthetic noisy point clouds and real-world scanned point clouds. Fig. REF  shows the denoised results of different methods on a synthetic model with the noise level of 0.5% and the point density of 50k. Compared with them, LBF achieves the best denoising result while faithfully preserving detailed geometric features. Besides, our LBF suppresses heavy noise better than competitors on real-world data. For example, Fig. REF  shows the denoised result of various methods on the real-world scanned dataset [6]}.
Again, our method produces a more desirable result that contains no outliers.
